For an automatic control of an automobile, or a driving assistance to a driver or the like, it is important to detect a road traveling lane appropriately and stably, from images taken by a camera. Normally, marking lines are painted on a road surface in accordance with various objects, such as lane boundary lines for defining a boundary of a traveling lane (traffic lane), to be mixed with solid lines or broken lines, marking lines made in a different form such as a block-like form, marking lines of different colors such as white or yellow, and further a complex of those marking lines.
For instance, FIG. 3 shows an example of an image (DS) including marking lines on a road with 2 vehicle lanes in the vicinity of a tunnel. As a lane boundary line (LB) indicative of a left boundary of a traveling lane (DL), a marking line of a white or yellow rigid line, at the inner side of which a marking line of a white block-like marking line has been used for a traveling guide line (LG). Also, as a lane boundary line (RB) indicative of a right boundary of a traveling lane (DL), a marking line of a white or yellow broken line, at the inner side of which a marking line of a white block-like marking line has been used for a traveling guide line (RG). In general, each width of those lanes is set to be 20 cm, the length of a painted portion of the marking line of broken line is set to be 8 m, and each space portion between the painted portions is set to be 12 m. The width of the block-like marking line is set to be 30 cm, the length of its painted portion is set to be 2-3 m, and each space portion between the painted portions is set to be 2-3 m. In the present application, the lane boundary line or the traveling guide line is meant by the marking line as viewed from its function, whereas when the white line or yellow line on the road surface itself is indicated, it is called as a lane mark.
With respect to the device for detecting the road traveling lane which is defined by the various marking lines as described above, various types have been proposed in the past, as disclosed in Patent document 1, for example. In this document, with respect to a vehicle lane determination device and a vehicle controller, in order to properly set a predetermined reference line for a vehicle, from a plurality of marking lines which are detected and adjacent to each other, it is so constituted as follows. That is, it is so described that the marking lines drawn on the surface of a road is detected from an image taken by a camera, and the marking lines to be a pair of white lines dividing a traveling lane are extracted from them. Then, the interval between the pair of marking lines extracted as the white lines is detected. Under a situation where the interval between the pair of marking lines extracted as the white lines is detected, when the plurality of marking lines adjacent to each other are detected on at least one side of the road from the image taken by the camera, based on the interval between the pair of marking lines as the white lines detected at that time, the pair of marking lines having an interval closest to the interval are extracted as the white lines.
Also, in Patent document 2, in order to detect a traffic lane boundary stably, there is proposed such a traffic lane boundary detector as constituted below. That is, it is provided with first contour line information detection means, sensitivity of which is set for spatial density change of original image data comparatively high and extracts a first contour line information from the image data, second contour line information detection means, sensitivity of which is set for spatial density change of original image data comparatively low and extracts a second contour line information from the image data, and contour extraction means for extracting outermost contour information of a group of white lines from the first and second contour line information detection means, so that the position of traffic lane boundary is set on the basis of the outermost contour information. It is so described that one includes information about edges corresponding to gaps between white lines, with the sensitivity being set for spatial density change to be high, whereas the other one does not include it, so that cancellation of the information about edges corresponding to the gaps will be easily made.
Furthermore, in Patent document 3, for the same object as described above, there is proposed such a traffic lane boundary detector as constituted below. That is, an outermost contour extraction section (reference numeral 15 in the Patent document 3. Same, hereinafter) extracts an outermost contour information of a group of white lines based on the contour data including the original image data stored in a frame buffer section (13) and the positional information of edge detected by an edge detection section (14). It is described that the outermost contour extraction section (15) determines whether or not the edge corresponds to the gaps generate between the white lines to constitute the group of white lines, based on the contour data including the positional information of the edge extracted from the original image data, and deletes the edge corresponding to the gaps from the contour data.
And, in Patent document 4, for the same object as described above, there is proposed a device for detecting a traffic lane boundary as constituted below. That is, a traveling lane of a mobile body including traffic lane in a predetermined area is taken by image pickup means, to obtain image data. Based on the obtained image data, density histograms are provided, and aggregation of the density histograms is detected, to be grouped. Then, among the grouped histograms, first center positions which are the centers of individual histograms, are detected, and based on the first center positions, second center positions which correspond to the centers in the grouped aggregation of histograms, are detected. Furthermore, it is described that based on the second center positions between the histograms in different groups of histograms, the center of a lane mark or lane mark groups having a plurality of lane marks is detected, to determine the position of the lane mark boundary, so that a stable detection of the lane mark boundary can be achieved, with the histograms produced on the basis of the image data.
On the other hand, with respect to an image processing technique, Hough conversion has been widely known as a method for detecting a straight line, as explained in Non-patent document 1 as listed below, for example. The Hough conversion has been known as the method for detecting a straight line to be robust against noise, and characterized in that during a process for converting points on a (x, y) coordinate system into a curve on a (ρ, θ) polar coordinate system, the curve on the (ρ, θ) polar coordinate system converted from edge points provided on a common straight line on the (x, y) coordinate system, intersects at a single point. Furthermore, recently, in a computer vision, RANSAC (Random Sample Consensus) which is a kind of Robust paradigm, has become popular, as explained in detail in Non-patent document 2 as listed below, for example. Also, RANSAC has been explained in Non-patent document 3 as listed below.    Patent document 1:    Japanese Patent Laid-open Publication 2003-168198    Patent document 2:    Japanese Patent Laid-open Publication 2003-187227    Patent document 3:    Japanese Patent Laid-open Publication 2003-187252    Patent document 4:    Japanese Patent Laid-open Publication 2003-178399    Non-patent document 1:    Pages 127 and 128 of “Introduction to Computer Image Processing” edited by Hideyuki Tamura, first issue, first print, published by Soken Shuppan, on Mar. 10, 1985    Non-patent document 2:    Pages 381-395 of “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography” written by Martin A. Fischero and Robert C. Bolles, vol. 24(6), published by Graphics and Image Processing, in 1981    Non-patent document 3:    Pages 101-107 of “Multiple View Geometry in Computer Vision” written by Richard Hartley and Andrew Zisserman, published by Cambridge University Press., in August, 2000